Genet. Res., Camb. (2010), 92, pp. 103–113.
doi:10.1017/S0016672310000108
103
f Cambridge University Press 2010
Genetic variation in senescence marker protein-30 is
associated with natural variation in cold tolerance in
Drosophila
K A T I E J. C L O W E R S 1 *, R I C H A R D F. L Y M A N 2 , T R U D Y F. C. M A C K A Y 2 , 3
T H E O D O R E J. M O R G A N 1 #
AND
1
The Division of Biology and The Ecological Genomics Institute, Kansas State University, Manhattan, KS 66506, USA
Department of Genetics, North Carolina State University Raleigh, NC 27695, USA
3
The W. M. Keck Center for Behavioral Biology, North Carolina State University Raleigh, NC 27695, USA
2
(Received 4 November 2009 and in revised form 9 March 2010 )
Summary
A comprehensive understanding of the genetic basis of phenotypic adaptation in nature requires the
identification of the functional allelic variation underlying adaptive phenotypes. The manner in
which organisms respond to temperature extremes is an adaptation in many species. In the current
study, we investigate the role of molecular variation in senescence marker protein-30 (Smp-30) on
natural phenotypic variation in cold tolerance in Drosophila melanogaster. Smp-30 encodes a
product that is thought to be involved in the regulation of Ca2+ ion homeostasis and has been
shown previously to be differentially expressed in response to cold stress. Thus, we sought to assess
whether molecular variation in Smp-30 was associated with natural phenotypic variation in cold
tolerance in a panel of naturally derived inbred lines from a population in Raleigh, North Carolina.
We identified four non-coding polymorphisms that were strongly associated with natural phenotypic
variation in cold tolerance. Interestingly, two polymorphisms that were in close proximity to one
another (2 bp apart) exhibited opposite phenotypic effects. Consistent with the maintenance of a
pair of antagonistically acting polymorphisms, tests of molecular evolution identified a significant
excess of maintained variation in this region, suggesting balancing selection is acting to maintain this
variation. These results suggest that multiple mutations in non-coding regions can have significant
effects on phenotypic variation in adaptive traits within natural populations, and that balancing
selection can maintain polymorphisms with opposite effects on phenotypic variation.
1. Introduction
For most organisms, the manner in which individuals
cope with temperature extremes represents an ecologically and evolutionarily important phenotype that
can influence many aspects of fitness, local adaptation
and the range of habitats in which populations can
persist (Leather et al., 1993 ; Clarke, 1996 ; Zhen &
Ungerer, 2008). Temperature extremes represent a
major abiotic stressor that all organisms must be able
to manage, either through adaptive genetic responses
to environmental variation, plastic responses to the
* Present address: Laboratory of Genetics, University of
Wisconsin–Madison, 425-G Henry Mall, Madison, WI 53706,
USA.
# Corresponding author. The Division of Biology, 116 Ackert Hall,
Manhattan, KS 66502, USA. Tel: 785-532-6126. Fax: 785-5326653. e-mail: [email protected]
environment, or some combination of both genetic
and plastic mechanisms (Hoffmann & Parsons, 1991 ;
Ayrinhac et al., 2004; Hoffmann et al., 2005 ;
Hoffmann & Willi, 2008). This is especially true for
organisms that are isothermal with their environments (e.g. insects and plants). For such species, the
presence of segregating genetic variation for thermal
tolerance facilitates survival in a changing environment or expansion outside the historical habitat
(Hoffmann et al., 2003).
Drosophila melanogaster has had great success
adapting to novel environments during its expansion
to its current worldwide species distribution. D. melanogaster originated in central tropical Africa, expanded and colonized more temperate climates in
Europe and Asia, and more recently has colonized
the rest of the world (David & Capy, 1988). This
K. J. Clowers et al.
movement out of Africa into more temperate regions
of the world required D. melanogaster to cope with
many novel thermal stresses. The manner in which
populations respond to thermal extremes consists
of responses to both high- and low-temperature extremes. Although both ends of the thermal spectrum
are ecologically important, resistance to low temperatures appears to be a major climatic adaptation
among Drosophila species (Gibert & Huey, 2001;
Gibert et al., 2001; David et al., 2003) and populations (Gibert & Huey, 2001; Gibert et al., 2001;
Hoffmann et al., 2002 ; Ayrinhac et al., 2004 ; Schmidt
& Paaby, 2008) in nature.
Many studies have shown that cold tolerance
phenotypes represent an important adaptation in
Drosophila ; however, a comprehensive understanding
of this adaptation requires a complete description of
the genetic architecture influencing variation in the
trait, as well as the identification of the functional
allelic variants underlying such phenotypes. Our
knowledge of the genetic architecture of cold tolerance has included studies focused on artificial selection (Tucic, 1979 ; Chen & Walker, 1993 ; Anderson
et al., 2005) ; quantifying genetic variation along cold
tolerance clines (Gibert & Huey, 2001 ; Hoffmann
et al., 2002 ; Ayrinhac et al., 2004 ; Kimura, 2004;
Collinge et al., 2006) ; associations of variation in cold
tolerance with polymorphisms in candidate genes
(Anderson et al., 2003; Collinge et al., 2008) ; quantum trait locus (QTL) analyses of cold tolerance
phenotypes in recombinant inbred lines (RILs)
(Morgan & Mackay, 2006; Norry et al., 2007, 2008) ;
identification of cold tolerant mutations (Takeuchi
et al., 2009) and genetic/genomic changes in gene expression in response to artificial selection on cold
tolerance (Telonis-Scott et al., 2009) or various cold
stressors (Goto, 2000, 2001 ; Qin et al., 2005 ; Sinclair
et al., 2007). These studies have clearly demonstrated
that substantial heritable genetic variation exists for
cold tolerance and that this variation is influenced by
a large number of genes. Combining this data across
studies exposes a number of promising candidate gene
regions within the genome between cytological positions 73A-90B6. This region has been identified by
QTL analyses of two different sets of RILs (Morgan
& Mackay, 2006 ; Norry et al., 2008). Morgan &
Mackay (2006) localized a QTL between 76B and
87B, while Norry et al. (2008) identified a broader
overlapping QTL between 73A and 90B6. Within the
region between 73A and 90B6 many cold tolerance
candidate genes exist including desaturase 2 (87B ;
Greenberg et al., 2003; Overgaard et al., 2005; Coyne
& Elwyn, 2006) as well as multiple cold responsive
candidate genes including Frost (85E ; Goto, 2001;
Qin et al., 2005 ; Sinclair et al., 2007) and senescence
marker protein-30 (Smp-30) (88D ; (Goto, 2000 ; Qin
et al., 2005 ; Sinclair et al., 2007). It should be noted
104
that although this broad QTL interval includes the
left edge of the inversion, In(3R)Payne (Kennington
et al., 2006), all of the cold tolerance candidate loci
discussed above are outside the inversion.
Smp-30 is a particularly compelling candidate gene
for cold tolerance as it has been shown to be highly
responsive to cold stress (Goto, 2000 ; Qin et al., 2005;
Sinclair et al., 2007). Goto (2000) found Smp-30 was
up-regulated after 1–2 days of acclimation to 15 xC,
while both Qin et al. (2005) and Sinclair et al. (2007)
found Smp-30 was down-regulated after a shorter
2-h treatment at 0 xC. Smp-30 contains three exons
that encode a 33.3 kDa protein (Goto, 2000). Additionally, although Smp-30 is not well studied in
D. melanogaster, there are multiple biochemical
studies in various mammalian cell lines suggesting
that it has a role in Ca2+ ion homeostasis and signalling (Fujita et al., 1998 ; Inoue et al., 1999 ; Son
et al., 2008). This is physiologically significant because
a shift in ion homeostasis (e.g. Ca2+) can lead to a
change in the electrochemical potential across cell and
organelle membranes, which lead to cold-induced
immobilization or injury via several mechanisms
(Hochachka, 1986 ; Pullin & Bale, 1988; Kelty et al.,
1996 ; Kostal et al., 2004, 2007 ; Takeuchi et al., 2009).
Finally, other physiological studies have found that
calcium is required for rapid cold-hardening protection in the Antarctic midge (Teets et al., 2008),
as chilling causes changes in membrane properties
that increase calcium flux, which, in turn, activates
calcium-dependent protein kinases and transcription
factors that regulate cold-specific proteins and genes.
Given the genetic data for Smp-30 and the known
physiological importance of Ca2+ and ion homeostasis and signalling in cold-stress phenotypes in
insects, we sought to determine if natural molecular
variation in Smp-30 is associated with natural
phenotypic variation in cold tolerance. To do this, we
used an association mapping approach, which surveys
sequence and phenotypic variation within a natural
population of flies and statistically tests, if polymorphisms within the candidate gene explain the
natural phenotypic variation in the trait. In this study,
we are able to link molecular variation in the 2000
base pair coding and non-coding sequence of Smp-30
with natural phenotypic variation in cold tolerance in
D. melanogaster.
2. Materials and methods
(i) Drosophila stocks
The lines used in this experiment were inbred lines
created from a sample of females collected at the
Raleigh, NC Farmer’s Market in the spring of 2002.
Wild-caught females were used to initiate isofemale
lines, which were subsequently inbred (via full-sib
Smp-30 and variation in cold tolerance
mating) for 20 generations, resulting in a panel of 350
fully inbred yet naturally derived lines. This panel of
lines has preserved the natural variation among lines,
while eliminating variation within lines. In the current
study, 257 randomly selected lines were used. Flies
were reared on a standard cornmeal–agar–molasses
medium at 25 xC and 70 % humidity on a 12 : 12
light : dark cycle.
(ii) Phenotypic assays
Cold tolerance was quantified by measuring the chillcoma recovery time developed by Morgan & Mackay
(2006). Four replicates of 25 flies per line per sex were
measured in a randomized design. Five-to-seven-dayold same-sex flies were subjected to 0 xC for 3 h. Upon
removal from the cold, flies were returned to room
temperature (23 xC) and the percent recovery from
chill-coma was recorded after 22 min at 23 xC. This
experimentally determined time point was chosen
because this was the average 50 % recovery time point
in a pilot study of 13 randomly chosen lines from this
panel (Supplemental Figure 1). Cold tolerance was
measured on each line during the summer of 2006.
(iii) Quantitative genetic analyses
Analyses of variance (ANOVA) were used to partition variance in cold tolerance among the inbred
lines using the following model, y=m+L+S+Lr
S+e, where y is the percent recovery from chill coma,
m is the overall mean, L is the random effect of line,
S is the fixed effect of sex and e is the residual variance. We estimated the total genotypic variance (s2G)
2
among lines as s2G=sL2 +sLS
, where sL2 is the among
line variance component and s2LS is the variance
caused by the interaction of line and sex. The total
2
phenotypic variance was estimated as s2P=sG
+sE2 ,
2
where sE is the environmental variance. The broad
sense heritability of cold tolerance was estimated as
2
H2=sG
/sP2 . To compare the amount of genetic and
environmental variance, the coefficient of variance
was estimated. The coefficientpof
ffiffiffiffiffi genetic variance
was calculated as CVG =100r s2G =x̄, where x̄ is the
average percent of chill-coma recovery and s2G is
the genetic variation, while the coefficient of environmental
variation was calculated as CVE =100r
ffiffiffiffiffi
p
s 2E =x̄, where x̄ is the average percent of chill-coma
recovery and s2E is the environmental variance. All
analyses were carried out using SAS in PROC GLM
and VARCOMP procedures (SAS Institute).
(iv) Sequencing of Smp-30
DNA was extracted from single flies using the
Puregene DNA isolation kit (Gentra Systems) in the
summer of 2007. We sequenced approximately 2000
105
base pairs of the Smp-30 gene region, including 750
base pairs upstream of the translation start site and
100 base pairs downstream of the end of the coding
sequence for 125 lines (GenBank accession nos.
HM034321–HM034445). This was done using three
pairs of PCR primers that were designed to amplify
three roughly 600 bp amplicons spanning the Smp-30
gene region. Primers were designed using the published D. melanogaster sequence as a reference and
the Primer3 program (http://biotools.umassmed.edu/
bioapps/primer3_www.cgi). The three amplicons were
amplified per line using 30 ml PCR reactions, which
were subsequently sequenced in both directions by the
AGTC at the University of Kentucky. Sequences were
edited and contigs assembled in Seqman Pro (DNA*
Lasergene suite). Contigs were then aligned using
MUSCLE (http://www.ebi.ac.uk/Tools/muscle/index.
html) and the polymorphisms (both single nucleotide
polymorphisms (SNPs) and insertion/deletions) were
identified. For the remaining 132 lines, we genotyped
the majority of these polymorphisms in each line. This
genotyping was done via a combination of partial
direct sequencing, SNP-Restriction fragment length
polymorphism (Carbone et al., 2006), as well as allelespecific amplification of insertion deletion polymorphisms. Ultimately this resulted in a polymorphism
dataset that contained on average 214 genotyped lines
at each polymorphism. For the association mapping
analysis, polymorphisms were used only if the minor
allele frequency was at least 2%.
(v) Statistical analyses
(a) Linkage disequilibrium (LD)
Patterns of LD and significance tests were assessed
with TASSEL (http://www.maizegenetics.net). LD was
quantified as r2=(PiiPjjxPijPji)2/pi(1xpi)pj(1xpj ),
where pi and pj are the allele frequencies at the ith
and jth locus, respectively, and Pij are the haplotype
frequencies at loci i and j. Significance of pairwise
LD was determined using Fisher’s exact test. Allele
frequencies for each of the polymorphisms were estimated using PROC FREQ in SAS.
(b) Association mapping
Statistical associations between polymorphisms and
percent recovery from chill coma were determined by
a set of two-way ANOVAs. For each of the 79 polymorphisms, the variation among sex-specific linemeans was explained using the following model :
y=m+ M+ S+ Mr S+e, where y is the sex-specific
line-mean of percent recovery from chill coma, m is the
grand mean, M and S are the fixed effects of marker
allele and sex, respectively and e is the residual error.
The terms of primary interest in an association
K. J. Clowers et al.
(c) Molecular population genetic analyses
We used DnaSP Version 5.0 (Librado & Rozas, 2009)
to evaluate patterns of molecular variation within
Smp-30 and test for patterns of molecular evolution
that are consistent with departures from neutrality.
Molecular variation in Smp-30 was quantified by the
nucleotide diversity based on the number of segregating sites (hS ; Watterson, 1975), the average number of nucleotide differences between pairs of sites
(hp ; Nei & Tajima, 1981), as well as the number of
singleton sites (hg ; Fu & Li, 1993). To test for depar-
100
% Recovery from chill coma
analysis are the main effect of marker (M) and the
interaction effect of marker-by-sex (MrS), as these
correspond to the effect of overall or sex-specific
allelic variation on the phenotype of interest. In our
analysis, we tested 79 polymorphisms, thus to control
the type I error at an experiment wise level of less than
or equal to 0.05, we used the highly conservative
Bonferroni correction. For 79 polymorphisms, our
significance threshold was equal to P=0.00063 or
–log(P)=3.2007.
In addition to investigating the individual main
effects of markers, we also examined pairwise epistatic
interactions among polymorphisms in the Smp-30
gene region shown to be associated with percent
recovery from chill coma. To do this, the variation
among sex-specific line-means was explained using
the following model : y=m+Mi+Mj+S+MirMj+
MirS+MjrS+MirMjrS+e, where y, m, S and e
are identical to the analysis presented above, but now
Mi and Mj are the fixed marker effects of significant
polymorphisms identified above. In this epistatic
analysis, the terms of primary interest are the interaction effects between the two markers (i.e. MirMj
and MirMjrS), as these correspond to the effect of
overall or sex-specific epistatic effects on the phenotype of interest. This analysis of epistasis was applied
only to markers with significant main effect. One
limitation of the formal epistatic analysis is that to
detect interactions among two biallelic markers, it is
essential that all the four two-locus genotypes are
represented in the dataset. Unfortunately, for polymorphisms in strong LD or close physical linkage this
is not always the case. To deal with this problem,
we also performed analyses that investigated the influence of haplotype variation on percent recovery
in chill coma. To do this, we used a simple two-way
ANOVA similar to the single marker analysis presented above, except rather than using the fixed effects
of a marker and sex, we tested the fixed effects of
haplotype and sex. Haplotypes were constructed by
concatenating the two-, three- or four-way combinations of significant single markers into a haplotype
variable. For the epistatic and haplotype analysis, we
used a significance threshold of P=0.05.
106
80
60
40
20
0
0
30
60
90
120 150 180 210 240 270
Line number
Fig. 1. Variation in percent recovery from chill coma
among the inbred lines. The x-axis is the line number
sorted in rank order based on percent recovery from chill
coma, while the y-axis is the percentage of flies recovered
from chill coma at an experimentally determined time
point of 22 min at room temperature (see methods). Lines
with 0 % recovery in 22 min are susceptible to cold, while
lines with 100% recovery in 22 min are resistant to cold.
ture from neutrality, we used Tajima’s D (Tajima,
1989). Estimates of D were calculated for the entire
gene as well as for sliding window intervals spanning
the entire gene. For the sliding window analysis, we
used 50 bp windows with a step size of 10 bp.
3. Results
(i) Variation among lines
(a) Phenotypic variation
We observed abundant natural phenotypic variation
in cold tolerance among 255 natural inbred lines
sampled from the Raleigh, North Carolina farmer’s
market (Ayroles et al., 2009), by measuring the percentage of flies that recovered from chill coma during
a 22 min observation period at room temperature
after 3 h at 0 xC (F255,255=8.89; P<0.0001 ; Fig. 1,
Supplementary Figure 1). Variation among the lines
ranged from lines that were extremely susceptible to
cold (0 % recovered in the observation period) to lines
that were extremely resistant to cold (100 % recovered
in the observation period), and all intermediate cold
tolerance phenotypes (Fig. 1). However, on average
49.96 % of the flies were recovered during the 22-min
observation period, suggesting that our rapid percentage assay is near the 50 : 50 recovery point, which
corresponds to the assay point with maximum statistical power.
A significant portion of this phenotypic variation
was the result of sex-specific genetic effects, as
demonstrated by the highly significant line-by-sex
interaction term (F255,1471=2.23; P<0.0001). Finally,
Smp-30 and variation in cold tolerance
107
Upper P-value
>0·01
<0·01
<0·001
<0·0001
Lower R2
1·00
0·90
0·80
0·70
0·60
0·50
0·40
0·30
0·20
0·10
0·00
Fig. 2. Smp-30 polymorphisms. The gene structure of Smp-30 is depicted ; the ATG translation start site is at the
beginning of the 2nd exon. Seventy-nine polymorphisms were identified among the 255 lines. Patterns of LD are shown
below the gene structure, with P values from Fisher’s exact test above the diagonal and estimates of r2 below the diagonal.
consistent with the significant line and line-by-sex
terms in the ANOVA among lines, most of this variation in the percentage of flies recovered from cold
was the result of genetic effects, as established by large
broad-sense heritability estimates from pooled ana2
lyses, H2=0.707 (sG
=766.35 ; sP2 =1083.77) and sex2
2
specific analyses (HMale=0.710; HFemale
=0.705). In
addition, the pronounced genetic effect was consistent
with the large magnitude of the coefficient of genetic
variation (CVG=55.40), relative to the coefficient of
environmental variation (CVE=35.66). Although all
three of these estimates of variation (H2, CVG, and
CVE) are high for Drosophila, they are consistent with
other studies of chill-coma recovery time in D. melanogaster (Anderson et al., 2005; Morgan & Mackay,
2006).
(b) Molecular variation in the Smp-30 gene region
We sequenced approximately 2000 bp encompassing
the Smp-30 gene region in this set of wild-derived
inbred lines, including the exons, introns and 5k-UTR
(5k-untranslated region) and 3k-UTR, and 750 bp upstream of the translation start site. The gene region
was highly polymorphic, with 79 polymorphisms that
had minor allele frequency (MAF) of at least 2 %
(Fig. 2; Supplemental Table 1). Seventy-two of these
polymorphisms were SNPs, while seven were insertion–deletion (indel) polymorphisms. The majority
of these polymorphisms we located in non-coding
regions, including the putative 5k regulatory region,
introns, and the 3k-UTR (Fig. 2). Consistent with
the large number of polymorphisms, the estimates
K. J. Clowers et al.
(A)
108
Table 1. Results of significant genotype–phenotype
associations
Markera
MAFb
Nc
G-632A
A-630G
Del-603In, Del2
C-11T
0.17
0.44
0.43, 0.09
0.08
199 17.15
199 13.65
209 7.42
246 18.57
F
P
2ad
0.00004
16.44
0.00025 x11.04
0.00032 x12.29
0.00002
20.86
a
(B)
Marker names correspond to the common allele, the
position in base pairs relative to the ATG start site (i.e. the
beginning of exon 2), prefixed by the common allele and
followed by the minor allele. In cases where three allelic
classes were discovered, the minor alleles are listed in order
of descending allele frequency.
b
Minor allele frequency.
c
The sample size (N, number of lines) used in the ANOVA.
d
Mean difference in cold tolerance ( % recovery) between
homozygous genotypes (common homozygote – rare
homozygote).
(ii) Associations between phenotypic variation and
molecular polymorphism
Fig. 3. Phenotypic associations and balancing selection
in Smp-30. (a) Statistical associations between molecular
variation in Smp-30 and percent recovery from chill coma.
The x-axis is the position in base pairs relative to the ATG
start site (at the beginning of exon 2), while the y-axis is
the significance of each marker on a –log scale. The red
horizontal dashed line is the experiment-wise P<0.05
threshold given by the Bonferroni correction. The marker
names of the four highly significant polymorphisms
are noted next to the corresponding data point.
(b) Evolutionary analyses of molecular variation in
Smp-30 via a sliding window analysis of Tajima’s
D. * P<0.05, ** P<0.01.
of nucleotide diversity were hS=0.00976, hp=0.01213
and hg=0.00986.
We calculated LD between all pairs of polymorphisms (Fig. 2). We observed significant LD among
many polymorphisms ; however, the actual association between alleles (i.e. the absolute values of LD)
as measured by R2 was quite small, with a few exceptions where markers in close physical proximity
exhibited large R2 values. Thus, the widespread significance of the LD is likely an effect of our large
sample size rather than the presence of defined
haplotype blocks. The majority of the significant LD
was contained within an approximately 1000 bp block
at the 5k end of the gene and in an 800 bp block at
the 3k end of the gene (Fig. 2). Although there was
some significant LD between these blocks, the overall
frequency of significant LD declined with physical
distance and thus LD was reduced between polymorphisms from the 5k and 3k ends of the gene.
We tested for associations between cold tolerance and
each of the 79 polymorphisms identified within the
Smp-30 gene region. Figure 3 a summarizes the significance, on a xlog(P) scale, of these single-marker
analyses. We observed four markers that exhibited
highly significant associations with cold tolerance;
these include G-632A (P=0.00004), A-630G (P=
0.00025), Del-603In,Del2 (P=0.00032) and C-11T
(P=0.00002). All of these polymorphisms exceed the
highly conservative experiment-wise Bonferroni correction (P=0.05/79=0.00063 ; red dashed line in
Fig. 3 a). In addition, all of the markers are located
in non-coding portions of the Smp-30 gene region ;
three of the polymorphisms (G-632A, A-630G and
Del-603In,Del2) are located within 30 base pairs
of one another and are in the putative 5k regulatory
region of the gene (Goto, 2000), while the fourth
(C-11T) resides in the first intron, 11 base pairs upstream of the translation start site (Fig. 3 a). Two of
the significant polymorphisms (G-632A and C-11T)
have skewed allele frequencies, with frequencies of the
common allele equal to 0.83 and 0.92, respectively,
while the other two polymorphisms (A-630G and
Del-603In,Del2) have common allele frequencies near
0.50 (Supplemental Table 1).
The mean difference in cold tolerance between the
homozygous genotypes for the significant polymorphisms ranged from x12.29% to 20.86 % (Table 1).
However, the mean phenotypic differences attributable to each polymorphism should be interpreted
with caution because the polymorphisms are not
independent, due to LD (Fig. 2). Nearly all of the
significant polymorphisms were in strong LD with
each other (P<0.0001 ; Fisher’s exact test ; Fig. 2).
The single exception was the marker pair A-630G and
Smp-30 and variation in cold tolerance
109
(A)
(B)
(C)
(D)
Fig. 4. Phenotypic effects of the significantly associated regions on the Smp-30 gene. (a) Highly significant variation in
percent recovery from chill coma associated with the haplotypes in the 5k upstream region of the gene containing
significant markers : G-632A, A-630G and Del-603In,Del2 (F3,390=7.48 ; P<0.0001). (b) Variation in percent recovery
from chill coma associated with the marker C-11T. (c and d) Evidence for statistical independence of the effect of
polymorphisms within the 5k haplotype (i.e. G-632A, A-630G and Del-603In,Del2) and the C-11T polymorphism. Both
figures show the main effects of each polymorphism but lack of significant pairwise interactions between polymorphisms.
Panel (C) is for the marker pair G-632A by C-11T (F1,390=0.02 ; P=0.8947), while panel (D) is for the marker pair Del603In,Del2 by C-11T (F1,408=0.79 ; P=0.3739). The same pattern holds for marker pair A-630G by C-11T (F1,390=0.44;
P=0.5071) graph not shown.
C-11T, which exhibited significant but a less robust
level of LD (P<0.01; Fig. 2). Regardless of the lack
of independence caused by LD among the significant
polymorphisms, on a striking pattern that emerges
from this data is the finding that two of the significant
polymorphisms (G-632A and A-630G), which are
located only 2 bp apart in the 5k putative regulatory
region of Smp-30 (Goto, 2000), have opposite effects
on chill-coma recovery (Table 1). Marker G-632A
has a difference in chill–coma recovery between the
homozygous alleles [G (p=0.83) ; A (q=0.17)] of
16.44%, while marker A-630G has a difference in
chill–coma recovery between the homozygous alleles
[A (p=0.56) ; G (q=0.44)] of x11.04 %. This pattern
is noteworthy because it is unlikely that a pair of
polymorphisms in such a close physical linkage would
have opposite effects on the same trait unless there
was significant balancing selection acting to maintain
the pair of antagonistic polymorphisms.
The three significant polymorphisms in the 5k
putative regulatory region of Smp-30 (Goto, 2000)
were in strong LD with one another and formed only
four haplotypes (Fig. 4a). This three-marker haplotype was significantly associated with percent recovery from chill coma (F3,390=7.48; P<0.0001; Fig. 4a).
To test for epistastis among the significant polymorphisms, we separated the polymorphisms into two
regions : region 1 (Fig. 4 a) contained the set of three
physically linked markers (i.e. G-632A, A-630G and
Del-603In,Del2) in the 5k putative regulatory region of
Smp-30 (Goto, 2000), while region 2 was the C-11T
marker (Fig. 4b). We tested for epistasis between the
three possible pairwise combinations of markers in
region 1 and the single marker in region 2 and did not
detect any significant epistatic interactions in any
analysis (Fig. 4 c, d). This can be easily seen by the lack
of significant interaction effects between markers :
G-632A by C-11T (F1,390=0.02 ; P=0.8947; Fig. 4c),
K. J. Clowers et al.
Del-603In,Del2 by C-11T (F1,408=0.79 ; P=0.3739;
Fig. 4 d) and A-630G by C-11T (F1,390=0.44 ; P=
0.5071 ; graph not shown).
(iii) Molecular population genetics of Smp-30
We tested for evidence of non-neutral molecular
evolution by estimating Tajima’s D (Tajima, 1989).
Tajima’s D was estimated using the set of 137 complete sequences for the entire gene region and with an
analysis of 50 bp sliding windows (Fig. 3 b). Tajima’s
D over the entire gene region was positive (D=
0.6752), but not significantly different from zero.
However, when we performed a sliding window
analysis, we identified three regions where Tajima’s
D is significantly positive (Fig. 3b), indicating a pattern of molecular variation consistent with the maintenance of excess variation by balancing selection.
The region with the most significant Tajima’s D
values occurred in the putative 5k regulatory region of
the gene (Goto, 2000) between sites x581 and x650
(Fig. 3 b). Interestingly, this region overlaps the region
containing three polymorphisms (G-632A, A-630G
and Del-603In,Del2) that are significantly associated
with phenotypic variation in cold tolerance (Fig. 3 a;
Table 1), which are in strong LD, but that have
phenotypic effects that are in opposite directions
based on differences between the homozygous genotypes at each polymorphism. The common allele at
marker G-632A increases the percentage of flies recovered from chill coma, while the common allele at
markers A-630G and Del-603In,Del2 decrease the
percentage of flies recovered from chill coma.
4. Discussion
One of the primary goals of evolutionary genetics is
to identify the causal allelic variants that contribute
to phenotypic adaptation in nature (Lewontin, 1974;
Hoekstra & Coyne, 2007). In Drosophila, cold tolerance is a major climatic adaptation that has been
documented by the presence of phenotypic clines on
multiple continents (Gibert et al., 2001; Hoffmann
et al., 2002 ; Ayrinhac et al., 2004; Schmidt & Paaby,
2008) and also significantly influences the divergence
and climate matching among Drosophila species
(Gibert & Huey, 2001 ; Gibert et al., 2001). As a result
of these recently documented adaptive patterns, many
regions of the genome have also been identified that
influence cold tolerance in Drosophila, including the
candidate gene Smp-30 (Goto, 2000 ; Qin et al., 2005;
Sinclair et al., 2007), which encodes a protein product
involved in Ca2+ ion homeostasis and signalling.
However, to understand cold adaptation in nature,
we must link polymorphisms in functionally important loci like Smp-30 with ecologically relevant
natural phenotypic variation. To date, no study has
110
performed a detailed analysis of sequence variation
within Smp-30 and cold tolerance variation within
a natural population (but see Rako et al., 2007) to
identify the segregating allelic variants influencing
cold tolerance in nature.
Our study confirms that Smp-30 is a cold tolerance
gene (Goto, 2000 ; Qin et al., 2005; Sinclair et al.,
2007) and more importantly that there is a set of four
polymorphisms (G-632A, A-630G, Del-603In,Del2
and C-11T), three of which are in the putative 5k
regulatory region of the gene (Goto, 2000) and one
that is just upstream of the ATG start site, which are
strongly associated with variation in cold tolerance.
All of these polymorphisms are at appreciable frequencies within our population (MAFo0.08) and all
were in significant LD with one another. This significant LD makes it difficult to disentangle the individual influence of each polymorphism on variation
in cold tolerance. However, if we use the most conservative (i.e. the smallest ; A-630G) estimate for the
difference in the cold tolerance between homozygous
marker classes, we observe a change of 11% between
the homozygous genotypes, which is a dramatic effect
on the cold tolerance.
LD throughout the 2-kb Smp-30 gene region obstructs our ability to identify the quantitative trait
nucleotides (Long et al., 1998 ; Carbone et al., 2006)
responsible for the observed variation in cold tolerance. Thus, it is possible that G-632A, A-630G,
Del-603In,Del2 and C-11T are not the causal polymorphisms, but rather are in LD with other true
causal regulatory polymorphism upstream of these
markers. This is because markers G-632A, A-630G
and Del-603In,Del2 all reside within the first 200 base
pairs of our sequencing reads ; therefore it is possible
that by only sequencing 750 base pairs upstream of
the ATG start site we are missing true regulatory
polymorphisms upstream of these strongly associated
markers. Future studies should extend the sequencing
sufficiently far in the 5k and 3k directions outside of the
coding sequence to observe the ultimate decay of LD
between our significant polymorphisms and other
possible unidentified causal polymorphisms further
upstream. One other study has evaluated the association between a variation in 5k regulatory region
Smp-30 and natural variation in cold tolerance in an
Australian population (Rako et al., 2007). In this
study, Rako et al. (2007) used a pair of primers
that span our three significantly associated polymorphisms, G-632A, A-630G and Del-603In,Del2, but
found no significant association between their four
different length variant (330–342 bp) alleles. Rako
et al. (2007) used a somewhat atypical association
analysis experimental design ; however, they were able
to detect multiple associations between other temperature stress genes and thermal phenotypes, suggesting that their approach was robust. Thus, in order
Smp-30 and variation in cold tolerance
to evaluate disagreement between our studies we
placed their reverse primer sequence (5k-TTAAAAG
GTATAAAACAATCAACACTG) on our alignment
(at position x491). We were not able to place their
forward primer sequence as it occurs approximately
100 bp upstream of the start of our sequence reads. By
scanning the region flanked by their reverse primer
and the start of our sequence reads, we were able to
construct four plausible length variant alleles from
our sequence data. Although we cannot definitely
identify the molecular basis of Rako et al.’s (2007)
12 bp length variation (i.e. 330–342 bp) PCR polymorphism, we did observe two indel polymorphisms
within the region that would have been amplified by
their primers. The first 3 bp indel (Del-702In) was
not associated with cold tolerance, while the second
9–10 bp indel (Del-603In, Del2) was significantly
associated with cold tolerance (Table 1 ; Fig. 3). If we
combine the length variation attributable to each
of these indel markers and create a composite polymorphism, we generate six possible length variant
alleles, because marker Del-603In,Del2, has a common 9 bp deletion allele, an insertion allele and a rare
10 bp deletion allele (Supplemental Table 1). Thus the
alleles at this composite marker in order of length
from shortest to longest are 13, 12, 10, 9, 3 and 0 bp
deletion alleles. Four of these alleles are present in our
data (13, 12, 9 and 3 bp). If we repeat our association
analysis and test for the association between this
composite length polymorphism and percent recovery
from chill coma, we observe a nominally significant
association [F3,310=3.90 ; P=0.0093; –log(P)=2.03] ;
this does not exceed our multiple testing criteria
[P=0.0006 ; –log(P)=3.20; Fig. 3]. Thus, this analysis
of our composite length variant allele suggests that
the lack of association observed by Rako et al. (2007)
is likely not in disagreement with our data. However,
by decoupling this length variation by direct sequencing, we are able to detect a significant association
between cold tolerance and marker Del,-603In,Del2
as well as two other SNPs (G-632A and A-630G)
which could not be detected via the PCR product
length polymorphism.
This study shows that Smp-30 is associated with
natural phenotypic variation in cold tolerance ; however, the most complete picture of this association
emerges when the results of the association, LD,
and molecular evolutionary analyses are considered
together. The presence of multiple highly significant
polymorphisms in close proximity to one another
(2–30 bp apart), which are also in strong LD with one
another, but display opposite phenotypic effects (i.e.
G-632A and A-630G) suggests that the allelic variation at these polymorphisms is likely being maintained by balancing selection. This argument becomes
even more compelling when it is coupled, the Tajima’s
D analysis that identifies this same region as a region
111
of the gene with the maintenance of an excess variation (i.e. a positive Tajima’s D) suggesting that this
putative 5k regulatory region (Goto, 2000) between
sites x581 and x650 is not only significantly associated with natural phenotypic variation in chill coma
but is also being maintained by balancing selection.
These results suggest that multiple mutations in
non-coding regions can have significant effects on
phenotypic variation on adaptive traits within natural
populations, and that balancing selection can maintain physically linked polymorphisms with opposite
effects on phenotypic variation in nature.
We thank M. U. Nasser, S. Menon and L. H. Duncan for
assistance with flies used in this study and to L. C. Fallis
for comments on a previous version of this manuscript.
This work was supported by grants from the Kansas
State University Ecological Genomics Institute and the
Arthropod Genomic Center to TJM, The Kansas State
University McNair Scholar’s Program to KJC and National
Institutes of Health Grant GM-45146 to TFCM.
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